# LibLMFuzz: LLM-Augmented Fuzz Target Generation for Black-box Libraries

arXiv: https://arxiv.org/abs/2507.15058 · HTML: https://mlsanity.com/paper/2507.15058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Fuzz4All: Universal Fuzzing with Large Language Models](https://mlsanity.com/paper/2308.04748/) (2023)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing](https://mlsanity.com/paper/2403.03897/) (2024)
- [HOPPER: Interpretative Fuzzing for Libraries](https://mlsanity.com/paper/2309.03496/) (2023)
- [Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing](https://mlsanity.com/paper/2404.06856/) (2024)

## Built on by (most cited first, 3 of 3)

- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Orion: Fuzzing Workflow Automation](https://mlsanity.com/paper/2509.15195/) (2025)
- [SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses](https://mlsanity.com/paper/2609.23889/) (2026)

## Abstract (arXiv)

A fundamental problem in cybersecurity and computer science is determining whether a program is free of bugs and vulnerabilities. Fuzzing, a popular approach to discovering vulnerabilities in programs, has several advantages over alternative strategies, although it has investment costs in the form of initial setup and continuous maintenance. The choice of fuzzing is further complicated when only a binary library is available, such as the case of closed-source and proprietary software. In response, we introduce LibLMFuzz, a framework that reduces costs associated with fuzzing closed-source libraries by pairing an agentic Large Language Model (LLM) with a lightweight tool-chain (disassembler/compiler/fuzzer) to autonomously analyze stripped binaries, plan fuzz strategies, generate drivers, and iteratively self-repair build or runtime errors. Tested on four widely-used Linux libraries, LibLMFuzz produced syntactically correct drivers for all 558 fuzz-able API functions, achieving 100% API coverage with no human intervention. Across the 1601 synthesized drivers, 75.52% were nominally correct on first execution. The results show that LLM-augmented middleware holds promise in reducing the costs of fuzzing black box components and provides a foundation for future research efforts. Future opportunities exist for research in branch coverage.